Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6903-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 Oct 2025
Development and technical paper |
| 08 Oct 2025
| 08 Oct 2025
Enhanced land subsidence interpolation through a hybrid deep convolutional neural network and InSAR time series
Cited articles
Abdollahi, S., Pourghasemi, H. R., Ghanbarian, G. A., and Safaeian, R.: Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions, B. Eng. Geol. Environ., 78, 4017–4034, https://doi.org/10.1007/s10064-018-1403-6, 2019.
Amighpey, M. and Arabi, S.: Studying land subsidence in Yazd province, Iran, by integration of InSAR and levelling measurements, Remote Sens. Appl.: Soc. Environ., 4, 1–8, https://doi.org/10.1016/j.rsase.2016.04.001, 2016.
Andaryani, S., Nourani, V., Trolle, D., Dehghani, M., and Asl, A. M.: Assessment of land use and climate change effects on land subsidence using a hydrological model and radar technique, J. Hydrol., 578, 124070, https://doi.org/10.1016/j.jhydrol.2019.124070, 2019.
Azarakhsh, Z., Azadbakht, M., and Matkan, A.: Estimation, modeling, and prediction of land subsidence using Sentinel-1 time series in Tehran-Shahriar plain: A machine learning-based investigation, Remote Sens. Appl.: Soc. Environ., 25, 100691, https://doi.org/10.1016/j.rsase.2021.100691, 2022.
Azarm, Z.: Enhanced Land Subsidence Interpolation through a Hybrid Deep Convolutional Neural Network and InSAR Time Series, Zenodo [code], https://doi.org/10.5281/zenodo.12721120, 2024.
